Sparsity, Regularization and Causality in Agricultural Yield: The Case of Paddy Rice in Peru

• URL: http://arxiv.org/abs/2409.17298v1
• Date: Wed, 25 Sep 2024 19:16:54 GMT
• Title: Sparsity, Regularization and Causality in Agricultural Yield: The Case of Paddy Rice in Peru
• Authors: Rita Rocio Guzman-Lopez, Luis Huamanchumo, Kevin Fernandez, Oscar Cutipa-Luque, Yhon Tiahuallpa and Helder Rojas
• Abstract summary: This study integrates agricultural census data with remotely sensed time series to develop precise predictive models for paddy rice yield across various regions of Peru. By utilizing sparse regression and Elastic-Net regularization techniques, the study identifies causal relationships between key remotely sensed variables.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study introduces a novel approach that integrates agricultural census data with remotely sensed time series to develop precise predictive models for paddy rice yield across various regions of Peru. By utilizing sparse regression and Elastic-Net regularization techniques, the study identifies causal relationships between key remotely sensed variables-such as NDVI, precipitation, and temperature-and agricultural yield. To further enhance prediction accuracy, the first- and second-order dynamic transformations (velocity and acceleration) of these variables are applied, capturing non-linear patterns and delayed effects on yield. The findings highlight the improved predictive performance when combining regularization techniques with climatic and geospatial variables, enabling more precise forecasts of yield variability. The results confirm the existence of causal relationships in the Granger sense, emphasizing the value of this methodology for strategic agricultural management. This contributes to more efficient and sustainable production in paddy rice cultivation.

Related papers

• Anticipatory Understanding of Resilient Agriculture to Climate [66.008020515555]
  We present a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. We focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population.
  arXiv  Detail & Related papers  (2024-11-07T22:29:05Z)
• Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing [6.65506917941232]
  We focus on the task of crop yield prediction, specifically for soybean, wheat, and rapeseed crops in Argentina, Uruguay, and Germany. Our goal is to develop and explain predictive models for these crops, using a large dataset of satellite images, additional data modalities, and crop yield maps. For model explainability, we utilize feature attribution methods to quantify input feature contributions, identify critical growth stages, analyze yield variability at the field level, and explain less accurate predictions.
  arXiv  Detail & Related papers  (2024-07-11T08:23:46Z)
• Naïve Bayes and Random Forest for Crop Yield Prediction [0.0]
  This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Na"ive Bayes, K-Mean Clustering, and Random Forest.
  arXiv  Detail & Related papers  (2024-04-23T16:55:45Z)
• Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis data [0.0]
  This study aims to investigate whether machine learning-based yield prediction models can capably predict Kharif season rice yields at the district level in India. The methodology involved training 19 machine learning models on 20 years of climate, satellite, and rice yield data across 247 of Indian rice-producing districts. Results showed rice yields can be predicted with a reasonable degree of accuracy, with out-of-sample R2, MAE, and MAPE performance of up to 0.82, 0.29, and 0.16 respectively.
  arXiv  Detail & Related papers  (2024-03-12T13:31:13Z)
• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Extreme Gradient Boosting for Yield Estimation compared with Deep Learning Approaches [0.0]
  We propose a pipeline to process remote sensing images into feature-based representations that allow the employment of Extreme Gradient Boosting (XGBoost) for yield prediction. A comparative evaluation of soybean yield prediction within the United States shows promising prediction accuracies compared to state-of-the-art yield prediction systems based on Deep Learning.
  arXiv  Detail & Related papers  (2022-08-26T12:48:18Z)
• Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions [91.63716984911278]
  We introduce a novel Mixture of Normal-Inverse Gamma distributions (MoNIG) algorithm, which efficiently estimates uncertainty in principle for adaptive integration of different modalities and produces a trustworthy regression result. Experimental results on both synthetic and different real-world data demonstrate the effectiveness and trustworthiness of our method on various multimodal regression tasks.
  arXiv  Detail & Related papers  (2021-11-11T14:28:12Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [70.54920804222031]
  Most existing forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations. We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP.
  arXiv  Detail & Related papers  (2021-06-07T18:31:47Z)
• Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning [8.786816847837976]
  We use historical performance records from Uniform Soybean Tests (UST) in North America spanning 13 years of data to build a Long Short Term Memory - Recurrent Neural Network based model to dissect and predict genotype response in multiple environments. We deploy this deep learning framework as a 'hypotheses generation tool' to unravel GxExM relationships. We envision broad applicability of this approach (via conducting sensitivity analysis and "what-if" scenarios) for soybean and other crop species under different climatic conditions.
  arXiv  Detail & Related papers  (2020-06-24T16:20:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.